Federated Learning for Privacy-Preserving Threat Detection in Massive IOT Ecosystems: A Systematic Literature Review
Abstract
Federated learning (FL) has emerged as a principal architecture for privacy-preserving intrusion detection in Internet of Things (IOT) environments, motivated by the impossibility of transmitting raw device traffic to a centralised server at scale. A rapidly growing body of empirical work applies FL to IOT intrusion detection systems (IDS), yet no PRISMA-compliant synthesis of this literature exists. This systematic review addresses that gap. Sixty-one peer-reviewed journal papers, identified through a PRISMA 2020-compliant search of five electronic databases covering 2018–2026, were subjected to full-text extraction and quality assessment on six dimensions. Four research questions guided the synthesis, organised into four themes. On FL architecture and performance (RQ1), standard FEDAVG and its variants govern aggregation in 65% of papers; two papers exceeded their centralised detection baseline, attributing the gain to data-centric rather than aggregation-level mechanisms. On privacy rigour (RQ2), 80% of papers claim privacy on structural grounds only; a twelve-subcategory privacy taxonomy is established, distinguishing formal differential privacy, cryptographic secure aggregation, and homomorphic encryption from structural-only claims; adaptive noise scheduling reduces the differential privacy accuracy cost from 5.77 percentage points to 0.01 percentage points relative to a non-private baseline. On evaluation realism (RQ3), 85% of papers evaluate on simulation only and…
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